Uploaded November 2020 | Updated September 2026, 2 hours ago
In this video, I'll walk you through a common SQL coding interview question that is asked in many data science interviews. This SQL question tests your knowledge of SQL window functions, specifically using a PARTITION BY. I'd consider this a simple SQL window function so let's test your knowledge on this question before diving into more complicated problems. Follow along with me and head to the question below.
Link to the question: platform.stratascratch.com/coding/9899-percentage-of-total-spend?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
This series is for both beginner and intermediate data scientists and analysts interested in learning how to solve common data science interview questions in SQL. These are real data science interview questions. For some background context and an intro about what this series is about: bit.ly/36kKbxG
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
Timestamps:
Intro: (0:00)
Interview Question: (0:11)
Explore the Dataset: (0:38)
Solution Approach: (1:27)
Coding - JOIN The Tables: (3:05)
Coding - Explanation of the SQL Window Function: (4:22)
Coding - Finishing up the Solution: (6:37)
______________________________________________________________________
About The Platform:
I'm using StrataScratch, a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
I created this platform because I wanted to build a resource to specifically help prepare data scientists for their technical interviews and to generally improve their analytical skills. Over my career as a data scientist, I never was able to find a dedicated platform for data science interview prep. LeetCode and HackerRank were the closest but these platforms specifically serve the computer developer community so their questions focus more on algorithms that working with data.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#sql #datascience #sqlinterviews
In this video, I'll walk you through a common SQL coding interview question that is asked in many data science interviews. This SQL question tests your knowledge of SQL window functions, specifically using a PARTITION BY. I'd consider this a simple SQL window function so let's test your knowledge on this question before diving into more complicated problems. Follow along with me and head to the question below.
Link to the question: platform.stratascratch.com/coding/9899-percentage-of-total-spend?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
This series is for both beginner and intermediate data scientists and analysts interested in learning how to solve common data science interview questions in SQL. These are real data science interview questions. For some background context and an intro about what this series is about: bit.ly/36kKbxG
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
Timestamps:
Intro: (0:00)
Interview Question: (0:11)
Explore the Dataset: (0:38)
Solution Approach: (1:27)
Coding - JOIN The Tables: (3:05)
Coding - Explanation of the SQL Window Function: (4:22)
Coding - Finishing up the Solution: (6:37)
______________________________________________________________________
About The Platform:
I'm using StrataScratch, a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
I created this platform because I wanted to build a resource to specifically help prepare data scientists for their technical interviews and to generally improve their analytical skills. Over my career as a data scientist, I never was able to find a dedicated platform for data science interview prep. LeetCode and HackerRank were the closest but these platforms specifically serve the computer developer community so their questions focus more on algorithms that working with data.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#sql #datascience #sqlinterviews
![Multiple Solutions to Data Scientist Interview Question From Amazon [Rolling Average]](https://i.ytimg.com/vi/GeJUvdkJKEc/mqdefault.jpg "Multiple Solutions to Data Scientist Interview Question From Amazon [Rolling Average]
This data scientist interview question comes from Amazon and it’s really an interesting question that deals with rolling average, the type of average that we calculate from values in a certain timeframe and we repeat it for all dates in a longer time period.
Link to the question to follow along with me: https://platform.stratascratch.com/coding/10314-revenue-over-time?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timeline:
Intro: (0:00)
Interview Question: (0:28)
Framework to solve the problem: (1:40)
Understand your data: (2:36)
Formulate your approach: (6:12)
Common table expressions and interval function: (7:09)
Window Function Approach: (20:35)
Comparison of approaches / Optimization discussion: (25:45)
Conclusion: (28:08)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#AmazonDataScientistInterview")
![Solving an Airbnb Data Science Coding Interview Question | SQL Interview [Fav Host Nationality]](https://i.ytimg.com/vi/IKw0lkmBHNI/mqdefault.jpg "Solving an Airbnb Data Science Coding Interview Question | SQL Interview [Fav Host Nationality]
This SQL data science interview question was asked by Airbnb. I’ll cover both the question and answer and give a detailed explanation of the approach. I walkthrough each step of my answer, assumptions, approach, and explain every line of code I write. This is literally how I would answer every data science interview question and prepare for every data science interview at FAANG companies and others.
Link to the question: https://platform.stratascratch.com/coding/10073-favorite-host-nationality?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
The question has many concepts that are always tested during a data science interview. This SQL interview question requires 3 JOINs and 2 subqueries. There are some tricks tested where you have to identify the highest review score using the SQL max() function. And lastly, this coding interview question tests your knowledge on how to de-duplicate data.. This question covers concepts that are commonly found in data science interviews at Facebook and Google.
This series is for both beginner and intermediate data scientists and analysts interested in learning how to solve common data science interview questions in SQL. These are real data science interview questions. For some background context and an intro about what this series is about: https://bit.ly/36kKbxG
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timestamps:
Intro: (0:00)
Interview Question: (0:10)
Explore Dataset: (0:35)
Writing Out Approach: (01:57)
Coding The Solution: (4:40)
2nd Solution!: (10:41)
About The Platform:
Im using StrataScratch, a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
I created this platform because I wanted to build a resource to specifically help prepare data scientists for their technical interviews and to generally improve their analytical skills. Over my career as a data scientist, I never was able to find a dedicated platform for data science interview prep. LeetCode and HackerRank were the closest but these platforms specifically serve the computer developer community so their questions focus more on algorithms that working with data.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com")
![[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings](https://i.ytimg.com/vi/Ih6G5hnn30Q/mqdefault.jpg "[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings
Welcome back to our data project series on Rotten Tomatoes! In this video, we dive into the decision tree classifier, a popular machine learning algorithm used for classification tasks. Watch as we create and train a decision tree classifier with a maximum of three leaf nodes, and evaluate its performance on test data. We assess accuracy, precision, and recall values, and visualize the decision workflow using a plot tree method. We also compare the results with a random forest classifier and explore feature importance. Dont forget to subscribe and stay tuned for the next video in our series!
Watch the full tutorial:
📌 [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: https://youtu.be/cM12QtrhdLo
📌 [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings:
https://youtu.be/J4rheMtvu6g
Go to the project through the link below and follow along with me👇
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2
Timeline:
Intro: (0:00)
Decision tree classifier: (0:16)
Instantiating the decision tree classifier: (1:44)
Visualizing the decision workflow: (3:00)
Accuracy score improving: (5:30)
Random forest classifier: (7:46)
Take away: (10:25)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from me, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#datascienceproject")
![[Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings](https://i.ytimg.com/vi/J4rheMtvu6g/mqdefault.jpg "[Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings
Welcome back to our data project series from Rotten Tomatoes! In this video, we continue our journey to enhance our machine-learning models. We explore the power of feature selection and weighted random forest classifier to improve performance. Join us in the next video for more exciting insights. See you there!
Watch the previous parts:
📌 [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: https://youtu.be/cM12QtrhdLo
📌[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: https://youtu.be/Ih6G5hnn30Q
Go to the project through the link below and follow along with me👇
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3
Timeline:
Intro: (0:00)
Splitting the Data: (0:58)
Feature Selection: (1:26)
Weighted Random Forest Classifier: (2:47)
Smote technique: (3:41)
Take away: (5:18)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from me, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#datascienceproject")
